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The potential of multispectral vegetation indices feature space for quantitatively estimating the photosynthetic, non-photosynthetic vegetation and bare soil fractions in Northern China

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Abstract
Non-photosynthetic vegetation (NPV) is widely distributed in the arid and semi-arid area, especially in the sandy areas. The hyperspectral-based cellulose absorption index (CAI) is an accepted method for estimating the cover fractions of NPV. However, the spaceborne hyperspectral data currently available to us are very limited. In this study, we tried to identify one or more combinations based on the multispectral vegetation indices feature space model to quantitatively estimate the PV, NPV and bare soil fractions of the Otindag Sandy Land in northern China. Three frequently-used green vegetation indices, NDVI, EVI and OSAVI, and nine multispectral-based indices sensitive to NPV were used to examine the spatial patterns based on the field measured endmember spectra and non-growing and growing season Landsat-8 OLI image reflectance spectra. The capabilities of these different combinations were tested in this study area using mosaicked Landsat-8 OLI imagery. The results show that the feature space of different combinations based on the field measured spectra and image reflectance spectra has good consistency. The separability of feature space determines the availability of this model. The normalized difference senescent vegetation index (NDSVI) and brightness index (BI) were found to have greater potential to combine with the three selected green vegetation indices for simultaneous estimation of the fractional cover of PV, NPV, and bare soil in the Otindag Sandy Land because of their clear and separable feature space. We obtained the best and medium-precision estimates for NDVI-NDSVI (f(PV): RMSE=0.26; f(NPV): RMSE=0.17) and OSAVI-BI (f(PV): RMSE=0.27; f(NPV): RMSE=0.25) for 104 field observations.
Keywords
AUSTRALIAN TROPICAL SAVANNA, CROP RESIDUE COVER, OTINDAG SANDY LAND, EO-1 HYPERION, DYNAMICS, FIELD

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Chicago
Zheng, Guoxiong, Anming Bao, Xiaosong Li, Liangliang Jiang, Cun Chang, Tao Chen, and Zhihai Gao. 2019. “The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-photosynthetic Vegetation and Bare Soil Fractions in Northern China.” Photogrammetric Engineering and Remote Sensing 85 (1): 65–76.
APA
Zheng, G., Bao, A., Li, X., Jiang, L., Chang, C., Chen, T., & Gao, Z. (2019). The potential of multispectral vegetation indices feature space for quantitatively estimating the photosynthetic, non-photosynthetic vegetation and bare soil fractions in Northern China. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 85(1), 65–76.
Vancouver
1.
Zheng G, Bao A, Li X, Jiang L, Chang C, Chen T, et al. The potential of multispectral vegetation indices feature space for quantitatively estimating the photosynthetic, non-photosynthetic vegetation and bare soil fractions in Northern China. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. 2019;85(1):65–76.
MLA
Zheng, Guoxiong et al. “The Potential of Multispectral Vegetation Indices Feature Space for Quantitatively Estimating the Photosynthetic, Non-photosynthetic Vegetation and Bare Soil Fractions in Northern China.” PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 85.1 (2019): 65–76. Print.
@article{8617414,
  abstract     = {Non-photosynthetic vegetation (NPV) is widely distributed in the arid and semi-arid area, especially in the sandy areas. The hyperspectral-based cellulose absorption index (CAI) is an accepted method for estimating the cover fractions of NPV. However, the spaceborne hyperspectral data currently available to us are very limited. In this study, we tried to identify one or more combinations based on the multispectral vegetation indices feature space model to quantitatively estimate the PV, NPV and bare soil fractions of the Otindag Sandy Land in northern China. Three frequently-used green vegetation indices, NDVI, EVI and OSAVI, and nine multispectral-based indices sensitive to NPV were used to examine the spatial patterns based on the field measured endmember spectra and non-growing and growing season Landsat-8 OLI image reflectance spectra. The capabilities of these different combinations were tested in this study area using mosaicked Landsat-8 OLI imagery. The results show that the feature space of different combinations based on the field measured spectra and image reflectance spectra has good consistency. The separability of feature space determines the availability of this model. The normalized difference senescent vegetation index (NDSVI) and brightness index (BI) were found to have greater potential to combine with the three selected green vegetation indices for simultaneous estimation of the fractional cover of PV, NPV, and bare soil in the Otindag Sandy Land because of their clear and separable feature space. We obtained the best and medium-precision estimates for NDVI-NDSVI (f(PV): RMSE=0.26; f(NPV): RMSE=0.17) and OSAVI-BI (f(PV): RMSE=0.27; f(NPV): RMSE=0.25) for 104 field observations.},
  author       = {Zheng, Guoxiong and Bao, Anming and Li, Xiaosong and Jiang, Liangliang and Chang, Cun and Chen, Tao and Gao, Zhihai},
  issn         = {0099-1112},
  journal      = {PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING},
  keywords     = {AUSTRALIAN TROPICAL SAVANNA,CROP RESIDUE COVER,OTINDAG SANDY LAND,EO-1 HYPERION,DYNAMICS,FIELD},
  language     = {eng},
  number       = {1},
  pages        = {65--76},
  title        = {The potential of multispectral vegetation indices feature space for quantitatively estimating the photosynthetic, non-photosynthetic vegetation and bare soil fractions in Northern China},
  url          = {http://dx.doi.org/10.14358/pers.85.1.65},
  volume       = {85},
  year         = {2019},
}

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